Human Action Recognition Using Ordinal Measure of Accumulated Motion
This paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI) using...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2010/219190 |
| _version_ | 1818644637242359808 |
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| author | Daeyoung Oh Changick Kim Minjin Kim Jaeho Lee Wonjun Kim |
| author_facet | Daeyoung Oh Changick Kim Minjin Kim Jaeho Lee Wonjun Kim |
| author_sort | Daeyoung Oh |
| collection | DOAJ |
| description | This paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI) using image differences. Then the AMI of the query action video is resized to a N×N subimage by intensity averaging and a rank matrix is generated by ordering the sample values in the sub-image. By computing the distances from the rank matrix of the query action video to the rank matrices of all local windows in the target video, local windows close to the query action are detected as candidates. To find the best match among the candidates, their energy histograms, which are obtained by projecting AMI values in horizontal and vertical directions, respectively, are compared with those of the query action video. The proposed method does not require any preprocessing task such as learning and segmentation. To justify the efficiency and robustness of our approach, the experiments are conducted on various datasets. |
| first_indexed | 2024-12-17T00:18:01Z |
| format | Article |
| id | doaj.art-78da780b61904f6db0848ee09383d667 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-17T00:18:01Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-78da780b61904f6db0848ee09383d6672022-12-21T22:10:38ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-01201010.1155/2010/219190Human Action Recognition Using Ordinal Measure of Accumulated MotionDaeyoung OhChangick KimMinjin KimJaeho LeeWonjun KimThis paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI) using image differences. Then the AMI of the query action video is resized to a N×N subimage by intensity averaging and a rank matrix is generated by ordering the sample values in the sub-image. By computing the distances from the rank matrix of the query action video to the rank matrices of all local windows in the target video, local windows close to the query action are detected as candidates. To find the best match among the candidates, their energy histograms, which are obtained by projecting AMI values in horizontal and vertical directions, respectively, are compared with those of the query action video. The proposed method does not require any preprocessing task such as learning and segmentation. To justify the efficiency and robustness of our approach, the experiments are conducted on various datasets.http://dx.doi.org/10.1155/2010/219190 |
| spellingShingle | Daeyoung Oh Changick Kim Minjin Kim Jaeho Lee Wonjun Kim Human Action Recognition Using Ordinal Measure of Accumulated Motion EURASIP Journal on Advances in Signal Processing |
| title | Human Action Recognition Using Ordinal Measure of Accumulated Motion |
| title_full | Human Action Recognition Using Ordinal Measure of Accumulated Motion |
| title_fullStr | Human Action Recognition Using Ordinal Measure of Accumulated Motion |
| title_full_unstemmed | Human Action Recognition Using Ordinal Measure of Accumulated Motion |
| title_short | Human Action Recognition Using Ordinal Measure of Accumulated Motion |
| title_sort | human action recognition using ordinal measure of accumulated motion |
| url | http://dx.doi.org/10.1155/2010/219190 |
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